CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study

Yajun Li,Yue Huang,Zishu Zhang,Binsheng Zhao,Lawrence H Schwartz,Laurent Dercle,Lin Lu,Manjun Xiao,Daiqiang Li

doi:10.1038/s41598-018-36421-0

Abstract

We evaluated whether the optimal selection of CT reconstruction settings enables the construction of a radiomics model to predict epidermal growth factor receptor (EGFR) mutation status in primary lung adenocarcinoma (LAC) using standard of care CT images. Fifty-one patients (EGFR:wildtype = 23:28) with LACs of clinical stage I/II/IIIA were included in the analysis. The LACs were segmented in four conditions, two slice thicknesses (Thin: 1 mm; Thick: 5 mm) and two convolution kernels (Sharp: B70f/B70s; Smooth: B30f/B31f/B31s), which constituted four groups: (1) Thin-Sharp, (2) Thin-Smooth, (3) Thick-Sharp, and (4) Thick-Smooth. Machine learning algorithms selected and combined 1,695 quantitative image features to build prediction models. The performance of prediction models was assessed by calculating the area under the curve (AUC). The best prediction model yielded AUC (95%CI) = 0.83 (0.68, 0.92) using the Thin-Smooth reconstruction setting. The AUC of models using thick slices was significantly lower than that of thin slices (P < 10−3), whereas the impact of reconstruction kernel was not significant. Our study showed that the optimal prediction of EGFR mutational status in early stage LACs was achieved by using thin CT-scan slices, independently of convolution kernels. Results from the prediction model suggest that tumor heterogeneity is associated with EGFR mutation.

Highlights

Individualized cancer treatment strategies are enabled by radiomic signatures associated with a specific gene mutation
In this study, we evaluated whether the optimization of reconstruction settings (i.e. Thin/Thick slice thicknesses, Sharp/Smooth convolution kernels) could allow the construction of a better radiomic signature, derived from a large number of quantitative image features, to predict the epidermal growth factor receptor (EGFR) mutation status in primary lung adenocarcinoma (LAC) using standard of care CT imaging
We demonstrated that the optimal selection of reconstruction parameters on CT-scan could enhance the predictive value of a radiomics signature to identify EGFR mutation status in early-stage lung adenocarcinoma

Summary

Introduction

Individualized cancer treatment strategies are enabled by radiomic signatures associated with a specific gene mutation. Most models used qualitative and basic imaging features or a limited set of radiomic features[17,19]. Most radiomic studies used retrospective imaging datasets that had heterogeneous imaging settings[16,17,18,19] (e.g., reconstruction kernel, slice thickness) which could affect radiomic features and critically alter the accuracy of radiomic signatures[16,18,19,23,24,25,26]. In this study, we evaluated whether the optimization of reconstruction settings (i.e. Thin/Thick slice thicknesses, Sharp/Smooth convolution kernels) could allow the construction of a better radiomic signature, derived from a large number of quantitative image features, to predict the EGFR mutation status in primary lung adenocarcinoma (LAC) using standard of care CT imaging

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